Edge detection in MRI brain tumor images based on fuzzy C-means clustering

• Published in: Procedia computer science Vol. 126; pp. 1261 - 1270
• Main Authors: Zotin, Alexander, Simonov, Konstantin, Kurako, Mikhail, Hamad, Yousif, Kirillova, Svetlana
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2018
• Subjects: balance contrast enhancement technique; Canny operator; Edge detection; fuzzy C means; median filter; medical imaging; Canny operator; median filter; Edge detection; balance contrast enhancement technique; medical imaging; fuzzy C means
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2018.08.069

Nowadays, medical image processing is the most challenging and emerging field. Edge detection of MRI images is one of the most important stage in this field. The paper describes the proposed strategy to detect the edges of brain tumor from patient’s MRI scan images of the brain. At the first stage, this method includes some noise removal functions improving features that provides better characteristics of medical images for reliable diagnosis using Balance Contrast Enhancement Technique (BCET). The result of second stage is subjected to image segmentation using Fuzzy c-Means (FCM) clustering method. Finally, Canny edge detection method is applied to detect the fine edges. During the experimental study, we used images containing brain tumors that were characterized by different location, type of pathology, shape, size and density, as well as the size of the area of the affected tissue near the tumor space. Detection and extraction of tumor from MRI scan images of the brain is done using MATLAB software. The obtained results demonstrate some resistivity to a noise. Also, the accuracy of segmentation, in some cases of tumor pathology, was increased up to 10-15% regarding the expert estimates.